AB1556 MACHINE LEARNING APPROACH FOR CLASSIFICATION OF ARTHRITIS ACTIVITY STATE, USING DATA FROM A SINGLE ACCELEROMETER
Authors: P. Mielnik, A. Myhre Hjelle, A. Traseth, K. Tokarz, B. Pollen, M. Fojcik
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Machine learning (ML) potential is not fully exploited in diagnostics and follow up of autoimmune inflammatory rheumatic disease (AIIRD). It is despite the broader use of ML in, e.g. imaging diagnostics. The specific tools for AIIRD are lacking. <h3>Objectives</h3> This is an interim analysis of data from the first checkpoint of the proof-of-concept study on using accelerometer (ACC) data in follow up of patients with AIIRD. The main goal of the study was to investigate the value of single ACC data in the classification of arthritis activity status. <h3>Methods</h3> Subjects with AIIRD are enrolled in the study when they start of new treatment due to the disease activity. Several comorbidities, such as severe neurological and cardiological disorders, as wells as impaired mobility, are part of the protocol exclusion criteria. Volunteers without AIIRD who fulfil other inclusion/exclusion criteria are included as controls. The study was approved by local competent authorities and informed consent was given by all participants. We used data collected up till the first study checkpoint. This analysis covers nine patients with AIIRD (5 rheumatoid and four psoriasis arthritis) and 13 controls. Five patients had 3 study visits, 1 had 2 visits and 3 only one. Controls have only one visit per protocol. We analysed ACC data from 3 minutes of clapping using a home-brewed device based on Arduino nano 33 BLE with 6-axis MTU by Nordic Semiconductor. Data was divided into 6-second chunks that were found optimal in our prior study. We conducted binary classification between any/ no arthritis in any of the upper extremities. We used accuracy and area under curve (AUC) as an efficacy function derived from the receiver operating characteristic curve (ROC). We extracted 54 features from 3 ACC axis signal. The features encompassed, among others, Fourier's components, autoregression coefficients, median absolute deviation (MAD), variance, Fourier's entropy, etc. We built linear discriminant analysis (LDA) models on the train data that were controlled on the test data. We originally built models with all features. Then, we analysed the importance of each feature on the models. Eventually, we constructed the most effective available model formula based on 7 features: the first autoregression coefficient for the X axis, kurtosis for the Z axis, correlation between Y and Z axis, zero point cross count for the Y axis, Z axis MAD, X and Y axis skewness. As we had few subjects included, we built 210 models with all possible data combinations. To avoid model overfitting, we never used data for model training from a subject who was actually in test data. All calculation was performed with R (R Core Team, 2022 www.R-project.org), using package caret (M. Kuhn 2022, CRAN.R-project.org/package=caret). <h3>Results</h3> Median accuracy for all LDA models was 0.78, mean 0.72 (95%CI 0.69-0.76). Median AUC was 1.0, mean 0.82 (95%CI 0.77-0.86). Distribution of the result was skewed to upper values. However, some models showed very low efficacy in classification (Figure 1 A and B). <h3>Conclusion</h3> Our study shows that it is possible to predict if a patient has any arthritis activity. However, we observed some instability in the classification between models. It can be due to small amount of data but also due to other distractors, such as comorbidities or drugs. Further research on the area is required and continuation of our study is justified based on the current results. <h3>REFERENCES:</h3> NIL. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> Pawel Mielnik Consultant of: Galapagos, Anja Myhre Hjelle: None declared, Anne Traseth: None declared, Krzysztof Tokarz: None declared, Bjarte Pollen: None declared, Marcin Fojcik: None declare.d.